• Energy Research

Abstract In this paper we propose an innovation diffusion framework based on well-known Bass models to analyze and forecast national adoption patterns of photovoltaic installed capacity. This allows for interesting comparisons among several countries and in many cases highlights the positive effect of incentive policies in stimulating the diffusion of such a technology. In this sense, the Generalized Bass Model proves to be essential for modelling and forecasting. On this basis, we observe important differences in the investments made by countries in the PV sector and we are able to identify whether and when these investments obtained the expected results. In particular, from our analysis it turns out that in some cases incentive measures have been certainly effective in facilitating adoption, while in some others these have not been able to produce real feed-back. Moreover, our cross-country approach is able to forecast different stages in PV evolution: whereas some countries have already entered the mature stage of diffusion, others have just begun. This result may suggest various considerations about the competitive advantage of those countries that invested in alternative energy provisions. In spite of a very diversified scenario in terms of historical patterns of diffusion, we may report, as a general result, the fragile role of innovators for this special market and the dominance of imitative behaviour in adoptions.

Bioenergy is being increasingly used worldwide to generate energy from biogas, biomethane, and other biofuels, bringing significant environmental and economic benefits. In Italy, biogas can significantly contribute to the achievement of the renewable energy targets set at the national and European levels. The exploitation of this energy source in a particular area is determined by its environmental and anthropic properties, as well as by the incentive system and the political will of decision makers. This paper analyzes the socioeconomic drivers and natural conditions triggering bioelectricity production in Italian regions. The analysis proposed here was performed in two steps—first, by identifying groups of similar regions for some natural, social, and economic variables, and then by modeling the historical trajectory of bioelectricity production for each identified group with innovation diffusion models. As a general finding, regions pertaining to the same group in terms of natural and socioeconomic conditions revealed a similar production pattern for bioelectricity, as confirmed by the results of diffusion modeling. On the basis of the diffusion modeling procedure, some scenario simulations were performed, which suggested the set-up of suitable policy actions for each group of regions.

In 2013 registered nuclear power consumption in several countries, including France, Germany, and other OECD members, declined. In this paper, we focus on nuclear consumption leaders and explore, through diffusion models, whether and to what extent Fukushima accident had a short-term effect on these countries' consumption dynamics. Safety checks, performed after the accident caused temporary shutdowns in production but not all of them were significant enough to modify nuclear energy evolution. Then, we compared the evolutionary behavior estimated through the entire time series and that obtained by excluding the last three observations (2011–2013): what would the forecasts have been before Fukushima? Significant short-term effects were identified in 2011–2013 at the global level, for France, and South Korea, while they have not been identified for the US, Germany, and Russia. About the medium-term evolution predicted by the models, we identified countries with declining consumption (the US, France, Germany and South Korea) and with increasing consumption (China, Russia, and Canada). At the global level a declining trend is predicted.

This paper analyzes the ongoing transition to sustainable energy in Australia, moving from traditional large-scale plants to distributed renewable generation by studying the time series of coal and gas consumption as well as onshore wind and solar. Even though most of energy generation, especially in the form of electricity is currently being generated from coal and gas, a quantitative assessment of their evolution is necessary to understand whether, and to which extent, renewables are competing in the marketplace with conventional production means. A well-accepted innovation diffusion model is used to capture and interpret the underlying dynamics of the competitive transition in generation. The results show that renewables are exerting a competitive pressure on coal and collaborate with gas towards the transition. The view that gas should play a key role in transition is confirmed by our findings, because it is found to have a competitive role towards coal, while aiding the uptake of renewables.

This paper examines the role of hydropower in the context of the energy transition, using innovation diffusion models. The study analyzes time series data of hydropower generation from 1965 to 2022 by applying diffusion models and some other models, such as Prophet and ARIMA, for comparison purposes. The models are evaluated across diverse geographic regions, including America, Africa, Europe, Asia, and the Middle East, to determine their effectiveness in predicting hydropower generation trends. The analysis reveals that the GGM consistently outperforms other models in accuracy across all regions. In most cases, the GGM exhibits better performance compared to the Bass, ARIMA, and Prophet models, highlighting its potential as a robust forecasting tool for hydropower generation. This study emphasizes the critical role of accurate forecasting in energy planning and calls for further research to validate these findings and explore additional factors influencing hydropower generation evolution.

Abstract This paper studies some quantitative aspects of the energy transition in Germany, the Energiewende, which envisages the complete abandonment of nuclear power and a strong reliance on photovoltaic and wind energy for electricity provision. The major aim is to shed light on, and measure with indirect tools, the possible social effects on competition and substitution dynamics characterizing such transition. In doing so, an analysis on the innovation diffusion framework is proposed through the application of two diffusion models for a duopolistic competition, unrestricted and standard UCRCD, to the annual time series of consumption of nuclear and renewables (wind and solar energy) in Germany, in order to test empirically the presence of such substitution effect. The obtained results confirm this conjecture and show that renewables have exerted a significant and measurable effect in determining the observed decline of nuclear power consumption. In particular we find that the diffusion of renewables – wind and solar – is characterized by a high within word-of-mouth, testifying the widespread belief of Germans towards the energy transition, while the parallel diffusion of nuclear power is characterized by a highly negative cross word-of-mouth, due to the competing role exerted by renewables.

This paper proposes the application of a multivariate diffusion model, based on ordinary differential equations, to investigate the energy transition in Germany. Specifically, the model is able to analyze the dynamic interdependencies between coal, gas and renewables in the energy market. A system dynamics representation of the model is also performed, allowing a deeper understanding of the system and the set-up of suitable strategic interventions through a simulation exercise. Such simulation provides a useful indication of how renewable energy consumption may be stimulated as a result of well-specified policies.

The National Energy and Climate Plans (NECPs) of the EU Member States have established comprehensive goals for 2030 to speed up the process of energy transition. Though Italy was an innovator in the area of photovoltaics (PV) up until 2014, the subsequent collapse and stagnation of its PV market have revealed an intrinsic fragility, which makes reaching international targets in the future unclear. This study used the Generalized Bass Model in a multi-phase extension to offer insights into and perspectives on the Italian PV market with the use of new data at finer temporal and market-size scales. Our model-based evidence suggests the possibility of a remarkable structural change corresponding to the “reboot” period after the pandemic crisis. In this period, small- and large-scale PV adoption, after years of parallel pathways, have taken largely different routes. On the one hand, small-scale adoption exhibited a fast decline with the end of the post-COVID-19 incentive programs, thus confirming the traditional “addiction to incentive” issue. On the other hand, during the “reboot” period, large-scale installations showed, for the first time, symptoms of exponential growth. This is consistent with the possibility that, finally, this sector is on an autonomous growth path. The latter evidence might represent a critically important novelty in the Italian PV landscape, where firms—rather than households—take the lead in the process. Nonetheless, future public monitoring and guidance are both urgent requirements to avoid a further catastrophic fall in the residential PV market and to make the sustained growth of the large-scale PV industry a robust phenomenon.